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1. Epigenome-wide association study of global cortical volumes in generation Scotland: Scottish family health study

Author

Miruna Carmen Barbu, Mat Harris, Xueyi Shen, Stolicyn Aleks, Claire Green, Carmen Amador, Rosie Walker, Stewart Morris, Mark Adams, Anca Sandu, Christopher McNeil, Gordon Waiter, Kathryn Evans, Archie Campbell, Joanna Wardlaw, Douglas Steele, Alison Murray, David Porteous, Andrew McIntosh, and Heather Whalley

Subjects
dna methylation, epigenome-wide association study, cortical volumes, generation scotland, Genetics, QH426-470
Abstract

A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores. We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10−8, βrange = −1.46x10−6 to 9.59 × 10−7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level. These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.

Published
2022
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2. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Published
2023

4. Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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5. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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6. The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses

Author

Rosie M. Walker, Michael Chong, Nicolas Perrot, Marie Pigeyre, Danni A. Gadd, Aleks Stolicyn, Liu Shi, Archie Campbell, Xueyi Shen, Heather C. Whalley, Alejo Nevado-Holgado, Andrew M. McIntosh, Stefan Heitmeier, Sumathy Rangarajan, Martin O’Donnell, Eric E. Smith, Salim Yusuf, William N. Whiteley, and Guillaume Paré

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins’ plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8–19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.

Published
2024
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7. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

Author

Fu, Cynthia HY, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen R, Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N, Frokjaer, Vibe G, Ganz, Melanie, Garcia, Jose, Godlewska, Beata R, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C, Tosun, Duygu, Victor, Teresa A, Wei, Dongtao, Wise, Toby, Woodham, Rachel D, Zahn, Roland, Anderson, Ian M, Deakin, JF William, Dunlop, Boadie W, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H, Harmer, Catherine J, Kennedy, Sidney H, Knudsen, Gitte M, Mayberg, Helen S, Paulus, Martin P, Qiu, Jiang, Trivedi, Madhukar H, Whalley, Heather C, Yan, Chao-Gan, Young, Allan H, and Davatzikos, Christos

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Biomedical Imaging, Neurosciences, Brain Disorders, Clinical Research, Depression, Bioengineering, Serious Mental Illness, Major Depressive Disorder, Mental Health, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Neurological, Mental health, Humans, Depressive Disorder, Major, Prospective Studies, Reproducibility of Results, Brain, Neuroimaging, Magnetic Resonance Imaging, Artificial Intelligence, Classification, Biomarkers, Deep learning, Harmonization, Predictors, MRI, Public Health and Health Services, Psychiatry, Clinical sciences, Epidemiology, Clinical and health psychology
Abstract

BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Published
2023

8. Atypical Brain Aging and Its Association With Working Memory Performance in Major Depressive Disorder

Author

Adamson, Chris, Adler, Sophie, Alexander-Bloch, Aaron F., Anagnostou, Evdokia, Anderson, Kevin M., Areces-Gonzalez, Ariosky, Astle, Duncan E., Auyeung, Bonnie, Ayub, Muhammad, Bae, Jong Bin, Ball, Gareth, Baron-Cohen, Simon, Beare, Richard, Bedford, Saashi A., Benegal, Vivek, Bethlehem, Richard A.I., Beyer, Frauke, Blangero, John, Cábez, Manuel Blesa, Boardman, James P., Borzage, Matthew, Bosch-Bayard, Jorge F., Bourke, Niall, Bullmore, Edward T., Calhoun, Vince D., Chakravarty, Mallar M., Chen, Christina, Chertavian, Casey, Chetelat, Gaël, Chong, Yap S., Corvin, Aiden, Costantino, Manuela, Courchesne, Eric, Crivello, Fabrice, Cropley, Vanessa L., Crosbie, Jennifer, Crossley, Nicolas, Delarue, Marion, Delorme, Richard, Desrivieres, Sylvane, Devenyi, Gabriel, Di Biase, Maria A., Dolan, Ray, Donald, Kirsten A., Donohoe, Gary, Dorfschmidt, Lena, Dunlop, Katharine, Edwards, Anthony D., Elison, Jed T., Ellis, Cameron T., Elman, Jeremy A., Eyler, Lisa, Fair, Damien A., Fletcher, Paul C., Fonagy, Peter, Franz, Carol E., Galan-Garcia, Lidice, Gholipour, Ali, Giedd, Jay, Gilmore, John H., Glahn, David C., Goodyer, Ian M., Grant, P.E., Groenewold, Nynke A., Gudapati, Shreya, Gunning, Faith M., Gur, Raquel E., Gur, Ruben C., Hammill, Christopher F., Hansson, Oskar, Hedden, Trey, Heinz, Andreas, Henson, Richard N., Heuer, Katja, Hoare, Jacqueline, Holla, Bharath, Holmes, Avram J., Huang, Hao, Ipser, Jonathan, Jack, Clifford R., Jr., Jackowski, Andrea P., Jia, Tianye, Jones, David T., Jones, Peter B., Kahn, Rene S., Karlsson, Hasse, Karlsson, Linnea, Kawashima, Ryuta, Kelley, Elizabeth A., Kern, Silke, Kim, Ki-Woong, Kitzbichler, Manfred G., Kremen, William S., Lalonde, François, Landeau, Brigitte, Lerch, Jason, Lewis, John D., Li, Jiao, Liao, Wei, Liston, Conor, Lombardo, Michael V., Lv, Jinglei, Mallard, Travis T., Marcelis, Machteld, Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, Michael J., Mechelli, Andrea, Misic, Bratislav, Morgan, Sarah E., Mothersill, David, Ortinau, Cynthia, Ossenkoppele, Rik, Ouyang, Minhui, Palaniyappan, Lena, Paly, Leo, Pan, Pedro M., Pantelis, Christos, Park, Min Tae M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, Deirel, Binette, Alexa Pichet, Pierce, Karen, Qian, Xing, Qiu, Anqi, Raznahan, Armin, Rittman, Timothy, Rodrigue, Amanda, Rollins, Caitlin K., Romero-Garcia, Rafael, Ronan, Lisa, Rosenberg, Monica D., Rowitch, David H., Salum, Giovanni A., Satterthwaite, Theodore D., Schaare, H. Lina, Schabdach, Jenna, Schachar, Russell J., Schöll, Michael, Schultz, Aaron P., Seidlitz, Jakob, Sharp, David, Shinohara, Russell T., Skoog, Ingmar, Smyser, Christopher D., Sperling, Reisa A., Stein, Dan J., Stolicyn, Aleks, Suckling, John, Sullivan, Gemma, Thyreau, Benjamin, Toro, Roberto, Traut, Nicolas, Tsvetanov, Kamen A., Turk-Browne, Nicholas B., Tuulari, Jetro J., Tzourio, Christophe, Vachon-Presseau, Étienne, Valdes-Sosa, Mitchell J., Valdes-Sosa, Pedro A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, Simon N., Vasung, Lana, Vértes, Petra E., Victoria, Lindsay W., Villeneuve, Sylvia, Villringer, Arno, Vogel, Jacob W., Wagstyl, Konrad, Wang, Yin-Shan S., Warfield, Simon K., Warrier, Varun, Westman, Eric, Westwater, Margaret L., Whalley, Heather C., White, Simon R., Witte, A. Veronica, Yang, Ning, Yeo, B.T. Thomas, Yun, Hyuk Jin, Zalesky, Andrew, Zar, Heather J., Zettergren, Anna, Zhou, Juan H., Ziauddeen, Hisham, Zimmerman, Dabriel, Zugman, Andre, Zuo, Xi-Nian N., Ho, Natalie C.W., Nogovitsyn, Nikita, Metzak, Paul, Ballester, Pedro L., Hassel, Stefanie, Rotzinger, Susan, Poppenk, Jordan, Lam, Raymond W., Taylor, Valerie H., Milev, Roumen, Frey, Benicio N., Harkness, Kate L., Addington, Jean, and Kennedy, Sidney H.

Published
2024
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10. An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank.

Author

Maria Viejo-Romero, Heather C Whalley, Xueyi Shen, Aleks Stolicyn, Daniel J Smith, and David M Howard

Subjects
Medicine, Science
Abstract

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr

Published
2024
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11. Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Author

Danni A. Gadd, Robert F. Hillary, Daniel L. McCartney, Liu Shi, Aleks Stolicyn, Neil A. Robertson, Rosie M. Walker, Robert I. McGeachan, Archie Campbell, Shen Xueyi, Miruna C. Barbu, Claire Green, Stewart W. Morris, Mathew A. Harris, Ellen V. Backhouse, Joanna M. Wardlaw, J. Douglas Steele, Diego A. Oyarzún, Graciela Muniz-Terrera, Craig Ritchie, Alejo Nevado-Holgado, Tamir Chandra, Caroline Hayward, Kathryn L. Evans, David J. Porteous, Simon R. Cox, Heather C. Whalley, Andrew M. McIntosh, and Riccardo E. Marioni

Subjects
Science
Abstract

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, blood protein markers of brain health are integrated with omics data to reveal DNA methylation differences that associate with these protein markers.

Published
2022
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12. Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
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13. Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Author

Gadd, Danni A., Hillary, Robert F., McCartney, Daniel L., Shi, Liu, Stolicyn, Aleks, Robertson, Neil A., Walker, Rosie M., McGeachan, Robert I., Campbell, Archie, Xueyi, Shen, Barbu, Miruna C., Green, Claire, Morris, Stewart W., Harris, Mathew A., Backhouse, Ellen V., Wardlaw, Joanna M., Steele, J. Douglas, Oyarzún, Diego A., Muniz-Terrera, Graciela, Ritchie, Craig, Nevado-Holgado, Alejo, Chandra, Tamir, Hayward, Caroline, Evans, Kathryn L., Porteous, David J., Cox, Simon R., Whalley, Heather C., McIntosh, Andrew M., and Marioni, Riccardo E.

Published
2022
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14. Computational modelling and assessment of depression : from neutral mechanisms and etiology to measurable behaviour

Author

Stolicyn, Aleksej, Series, Peggy, and Steele, Douglas

Subjects
616.85, depression, dopamine, amygdala, theory, computational, modelling, etiology, cognition, emotion, neurocognitive, assessment, diagnostics, classification
Abstract

Depression is a highly prevalent clinical condition which has been estimated to affect a growing part of the population in western countries. Alongside expenditure on diagnostics and treatment, there is a high economic impact due to lost productivity. Although a range of treatments are available, diagnoses are currently costly and require subjective assessment by a specialist. Moreover, treatment selection can be lengthy and can involve trial and error. To develop better diagnostics, stratification, and treatments for depression, we need a better understanding of the condition across different levels - from neural mechanisms to cognition and behaviour. Computational modelling is an emergent theory-driven approach which can aid linking data across different levels of analysis - from neural mechanisms and computations in the brain, to cognitive algorithms and observable behaviour. Some models integrate diverse findings and make predictions, while others enable inference of clinical measures which are not obvious in raw data. Modelling can lead to better understanding of depression, and in turn to better stratification and treatments. On the other hand, machine learning and classification methods can help detect clinically-relevant patterns in experimental data in a purely data-driven manner. This can lead to development of better screening and diagnostic methods. In the current work, we first review some of the most prominent neurocognitive theories of depression, as well as existing studies which used computational modelling methods. Based on our review, we argue that modelling can provide a rich set of tools for a better understanding of the condition. We then develop two novel computational modelling accounts of depression. In the first account, we propose an explicit mechanistic link between a robust behavioural negative bias effect and some of the widely reported or theorised neural aspects of depression - hyperactive amygdala and inhibited dopamine release. In the second account, we attempt to better explain depressive cognitive deficits and show how they can arise from depression-relevant etiological factors - altered valuation and controllability estimates. Finally, in the third part of this work we attempt to develop a novel system for detecting depressive symptoms based on a combination of face-tracking, eye-tracking and cognitive performance measures. We evaluate the system in a pilot experiment and show that a combination of measures can achieve better results than measures from each domain separately.

Published
2018

16. Atypical brain aging and its association with working memory performance in major depressive disorder

Author

Ho, Natalie C.W., primary, Bethlehem, Richard AI., additional, Seidlitz, Jakob, additional, Nogovitsyn, Nikita, additional, Metzak, Paul, additional, Ballester, Pedro L., additional, Hassel, Stefanie, additional, Rotzinger, Susan, additional, Poppenk, Jordan, additional, Lam, Raymond W., additional, Taylor, Valerie H., additional, Milev, Roumen, additional, Adamson, Chris, additional, Adler, Sophie, additional, Alexander-Bloch, Aaron F., additional, Anagnostou, Evdokia, additional, Anderson, Kevin M., additional, Areces-Gonzalez, Ariosky, additional, Astle, Duncan E., additional, Auyeung, Bonnie, additional, Ayub, Muhammad, additional, Bae, Jong Bin, additional, Ball, Gareth, additional, Baron-Cohen, Simon, additional, Beare, Richard, additional, Bedford, Saashi A., additional, Benegal, Vivek, additional, Bethlehem, Richard A.I., additional, Beyer, Frauke, additional, Blangero, John, additional, Blesa Cábez, Manuel, additional, Boardman, James P., additional, Borzage, Matthew, additional, Bosch-Bayard, Jorge F., additional, Bourke, Niall, additional, Bullmore, Edward T., additional, Calhoun, Vince D., additional, Chakravarty, Mallar M., additional, Chen, Christina, additional, Chertavian, Casey, additional, Chetelat, Gaël, additional, Chong, Yap S., additional, Corvin, Aiden, additional, Costantino, Manuela, additional, Courchesne, Eric, additional, Crivello, Fabrice, additional, Cropley, Vanessa L., additional, Crosbie, Jennifer, additional, Crossley, Nicolas, additional, Delarue, Marion, additional, Delorme, Richard, additional, Desrivieres, Sylvane, additional, Devenyi, Gabriel, additional, Di Biase, Maria A., additional, Dolan, Ray, additional, Donald, Kirsten A., additional, Donohoe, Gary, additional, Dorfschmidt, Lena, additional, Dunlop, Katharine, additional, Edwards, Anthony D., additional, Elison, Jed T., additional, Ellis, Cameron T., additional, Elman, Jeremy A., additional, Eyler, Lisa, additional, Fair, Damien A., additional, Fletcher, Paul C., additional, Fonagy, Peter, additional, Franz, Carol E., additional, Galan-Garcia, Lidice, additional, Gholipour, Ali, additional, Giedd, Jay, additional, Gilmore, John H., additional, Glahn, David C., additional, Goodyer, Ian M., additional, Grant, P.E., additional, Groenewold, Nynke A., additional, Gudapati, Shreya, additional, Gunning, Faith M., additional, Gur, Raquel E., additional, Gur, Ruben C., additional, Hammill, Christopher F., additional, Hansson, Oskar, additional, Hedden, Trey, additional, Heinz, Andreas, additional, Henson, Richard N., additional, Heuer, Katja, additional, Hoare, Jacqueline, additional, Holla, Bharath, additional, Holmes, Avram J., additional, Huang, Hao, additional, Ipser, Jonathan, additional, Jack, Clifford R., additional, Jackowski, Andrea P., additional, Jia, Tianye, additional, Jones, David T., additional, Jones, Peter B., additional, Kahn, Rene S., additional, Karlsson, Hasse, additional, Karlsson, Linnea, additional, Kawashima, Ryuta, additional, Kelley, Elizabeth A., additional, Kern, Silke, additional, Kim, Ki-Woong, additional, Kitzbichler, Manfred G., additional, Kremen, William S., additional, Lalonde, François, additional, Landeau, Brigitte, additional, Lerch, Jason, additional, Lewis, John D., additional, Li, Jiao, additional, Liao, Wei, additional, Liston, Conor, additional, Lombardo, Michael V., additional, Lv, Jinglei, additional, Mallard, Travis T., additional, Marcelis, Machteld, additional, Mathias, Samuel R., additional, Mazoyer, Bernard, additional, McGuire, Philip, additional, Meaney, Michael J., additional, Mechelli, Andrea, additional, Misic, Bratislav, additional, Morgan, Sarah E., additional, Mothersill, David, additional, Ortinau, Cynthia, additional, Ossenkoppele, Rik, additional, Ouyang, Minhui, additional, Palaniyappan, Lena, additional, Paly, Leo, additional, Pan, Pedro M., additional, Pantelis, Christos, additional, Park, Min Tae M., additional, Paus, Tomas, additional, Pausova, Zdenka, additional, Paz-Linares, Deirel, additional, Pichet Binette, Alexa, additional, Pierce, Karen, additional, Qian, Xing, additional, Qiu, Anqi, additional, Raznahan, Armin, additional, Rittman, Timothy, additional, Rodrigue, Amanda, additional, Rollins, Caitlin K., additional, Romero-Garcia, Rafael, additional, Ronan, Lisa, additional, Rosenberg, Monica D., additional, Rowitch, David H., additional, Salum, Giovanni A., additional, Satterthwaite, Theodore D., additional, Schaare, H. Lina, additional, Schabdach, Jenna, additional, Schachar, Russell J., additional, Schöll, Michael, additional, Schultz, Aaron P., additional, Sharp, David, additional, Shinohara, Russell T., additional, Skoog, Ingmar, additional, Smyser, Christopher D., additional, Sperling, Reisa A., additional, Stein, Dan J., additional, Stolicyn, Aleks, additional, Suckling, John, additional, Sullivan, Gemma, additional, Thyreau, Benjamin, additional, Toro, Roberto, additional, Traut, Nicolas, additional, Tsvetanov, Kamen A., additional, Turk-Browne, Nicholas B., additional, Tuulari, Jetro J., additional, Tzourio, Christophe, additional, Vachon-Presseau, Étienne, additional, Valdes-Sosa, Mitchell J., additional, Valdes-Sosa, Pedro A., additional, Valk, Sofie L., additional, van Amelsvoort, Therese, additional, Vandekar, Simon N., additional, Vasung, Lana, additional, Vértes, Petra E., additional, Victoria, Lindsay W., additional, Villeneuve, Sylvia, additional, Villringer, Arno, additional, Vogel, Jacob W., additional, Wagstyl, Konrad, additional, Wang, Yin-Shan S., additional, Warfield, Simon K., additional, Warrier, Varun, additional, Westman, Eric, additional, Westwater, Margaret L., additional, Whalley, Heather C., additional, White, Simon R., additional, Witte, A. Veronica, additional, Yang, Ning, additional, Yeo, B.T. Thomas, additional, Yun, Hyuk Jin, additional, Zalesky, Andrew, additional, Zar, Heather J., additional, Zettergren, Anna, additional, Zhou, Juan H., additional, Ziauddeen, Hisham, additional, Zimmerman, Dabriel, additional, Zugman, Andre, additional, Zuo, Xi-Nian N., additional, Frey, Benicio N., additional, Harkness, Kate L., additional, Addington, Jean, additional, and Kennedy, Sidney H., additional

Published
2024
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17. A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples

Author

Thng, Gladi, primary, Shen, Xueyi, additional, Stolicyn, Aleks, additional, Adams, Mark J., additional, Yeung, Hon Wah, additional, Batziou, Venia, additional, Conole, Eleanor L. S., additional, Buchanan, Colin R., additional, Lawrie, Stephen M., additional, Bastin, Mark E., additional, McIntosh, Andrew M., additional, Deary, Ian J., additional, Tucker-Drob, Elliot M., additional, Cox, Simon R., additional, Smith, Keith M., additional, Romaniuk, Liana, additional, and Whalley, Heather C., additional

Published
2024
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18. General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning

Author

Moodie, Joanna E., primary, Harris, Sarah E., additional, Harris, Mathew A., additional, Buchanan, Colin R., additional, Davies, Gail, additional, Taylor, Adele, additional, Redmond, Paul, additional, Liewald, David C. M., additional, Valdés Hernández, Maria del C., additional, Shenkin, Susan, additional, Russ, Tom C., additional, Muñoz Maniega, Susana, additional, Luciano, Michelle, additional, Corley, Janie, additional, Stolicyn, Aleks, additional, Shen, Xueyi, additional, Steele, Douglas, additional, Waiter, Gordon, additional, Sandu, Anca‐Larisa, additional, Bastin, Mark E., additional, Wardlaw, Joanna M., additional, McIntosh, Andrew, additional, Whalley, Heather, additional, Tucker‐Drob, Elliot M., additional, Deary, Ian J., additional, and Cox, Simon R., additional

Published
2024
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19. Hair glucocorticoids are associated with childhood adversity, depressive symptoms and reduced global and lobar grey matter in Generation Scotland

Author

Green, Claire, Stolicyn, Aleks, Harris, Mathew A., Shen, Xueyi, Romaniuk, Liana, Barbu, Miruna C., Hawkins, Emma L., Wardlaw, Joanna M., Steele, J. Douglas, Waiter, Gordon D., Sandu, Anca-Larisa, Campbell, Archie, Porteous, David J., Seckl, Jonathan R., Lawrie, Stephen M., Reynolds, Rebecca M., Cavanagh, Jonathan, McIntosh, Andrew M., and Whalley, Heather C.

Published
2021
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20. Publisher Correction: Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
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21. Exploring static and dynamic functional brain networks in adolescent depression using a co-produced novel irritability paradigm

Author

MacSweeney, Niamh, primary, Cabral, Joana, additional, Stolicyn, Aleks, additional, Louvet, Perrine, additional, Zafar, Simal, additional, Klinkhamer, Laura, additional, Casey, Hannah, additional, Atkinson, Kimberley, additional, Chan, Stella W. Y., additional, Lawrie, Stephen M., additional, Whalley, Heather C., additional, and Romaniuk, Liana, additional

Published
2023
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23. The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses

Author

Walker, Rosie, primary, Chong, Michael, additional, Perrot, Nicolas, additional, Pigeyre, Marie, additional, Gadd, Danni, additional, Stolicyn, Aleks, additional, Shi, Liu, additional, Whalley, Heather, additional, Nevado-Holgado, Alejo, additional, McIntosh, Andrew, additional, Heitmeier, Stefan, additional, Rangarajan, Sumathy, additional, O'Donnell, Martin, additional, Smith, Eric, additional, Yusuf, Salim, additional, Whiteley, William, additional, Campbell, Archie, additional, Shen, Xueyi, additional, and Pare, Guillaume, additional

Published
2023
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24. Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals

Author

Liu Shi, Colin R. Buchanan, Simon R. Cox, Robert F. Hillary, Riccardo E. Marioni, Archie Campbell, Caroline Hayward, Aleks Stolicyn, Heather C. Whalley, Mathew A. Harris, Jennifer Waymont, Gordon Waiter, Ellen Backhouse, Joanna M. Wardlaw, Douglas Steele, Andrew Mcintosh, Simon Lovestone, Noel J. Buckley, and Alejo J. Nevado‐Holgado

Subjects
mediation, neurodegeneration, plasma proteomics, sex‐related difference, vascular damage, Neurology. Diseases of the nervous system, RC346-429, Geriatrics, RC952-954.6
Abstract

Abstract Introduction This study aims to first discover plasma proteomic biomarkers relating to neurodegeneration (N) and vascular (V) damage in cognitively normal individuals and second to discover proteins mediating sex‐related difference in N and V pathology. Methods Five thousand and thirty‐two plasma proteins were measured in 1061 cognitively normal individuals (628 females and 433 males), nearly 90% of whom had magnetic resonance imaging measures of hippocampal volume (as N) and white matter hyperintensities (as V). Results Differential protein expression analysis and co‐expression network analysis revealed different proteins and modules associated with N and V, respectively. Furthermore, causal mediation analysis revealed four proteins mediated sex‐related difference in N and one protein mediated such difference in V damage. Discussion Once validated, the identified proteins could help to select cognitively normal individuals with N and V pathology for Alzheimer's disease clinical trials and provide targets for further mechanistic studies on brain sex differences, leading to sex‐specific therapeutic strategies.

Published
2021
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26. The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses

Author

Walker, Rosie M., primary, Chong, Michael, additional, Perrot, Nicolas, additional, Pigeyre, Marie, additional, Gadd, Danni A., additional, Stolicyn, Aleks, additional, Shi, Liu, additional, Campbell, Archie, additional, Shen, Xueyi, additional, Whalley, Heather C., additional, Nevado-Holgado, Alejo, additional, McIntosh, Andrew M., additional, Heitmeier, Stefan, additional, Rangarajan, Sumathy, additional, O’Donnell, Martin, additional, Smith, Eric E., additional, Yusuf, Salim, additional, Whiteley, William N., additional, and Paré, Guillaume, additional

Published
2023
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28. General and specific patterns of cortical gene expression as substrates of complex cognitive functioning

Author

Moodie, Joanna E, primary, Harris, Sarah E, additional, Harris, Mat A, additional, Buchanan, Colin R, additional, Davies, Gail, additional, Taylor, Adele, additional, Redmond, Paul, additional, Liewald, David, additional, Valdes Hernandez, Maria D, additional, Shenkin, Susan D, additional, Russ, Tom C, additional, Munoz Maniega, Susana C, additional, Luciano, Michelle, additional, Corley, Janie, additional, Stolicyn, Aleks, additional, Shen, Xueyi, additional, Steele, Douglas, additional, Waiter, Gordon, additional, Sandu-Giuraniuc, Anca, additional, Bastin, Mark, additional, Wardlaw, Joanna, additional, McIntosh, Andrew M, additional, Whalley, Heather C, additional, Tucker-Drob, Elliot M, additional, Deary, Ian, additional, and Cox, Simon R, additional

Published
2023
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29. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

Author

Fu, Cynthia, Fu, Cynthia, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen, Chen, Taolin, Choi, Ki, Fatt, Cherise, Frey, Benicio, Frokjaer, Vibe, Ganz, Melanie, Garcia, Jose, Godlewska, Beata, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strother, Stephen, Tosun, Duygu, Victor, Teresa, Wei, Dongtao, Wise, Toby, Woodham, Rachel, Zahn, Roland, Anderson, Ian, Deakin, J, Dunlop, Boadie, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian, Harmer, Catherine, Kennedy, Sidney, Knudsen, Gitte, Mayberg, Helen, Paulus, Martin, Qiu, Jiang, Trivedi, Madhukar, Whalley, Heather, Yan, Chao-Gan, Young, Allan, Davatzikos, Christos, Strigo, Irina, Fu, Cynthia, Fu, Cynthia, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen, Chen, Taolin, Choi, Ki, Fatt, Cherise, Frey, Benicio, Frokjaer, Vibe, Ganz, Melanie, Garcia, Jose, Godlewska, Beata, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strother, Stephen, Tosun, Duygu, Victor, Teresa, Wei, Dongtao, Wise, Toby, Woodham, Rachel, Zahn, Roland, Anderson, Ian, Deakin, J, Dunlop, Boadie, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian, Harmer, Catherine, Kennedy, Sidney, Knudsen, Gitte, Mayberg, Helen, Paulus, Martin, Qiu, Jiang, Trivedi, Madhukar, Whalley, Heather, Yan, Chao-Gan, Young, Allan, Davatzikos, Christos, and Strigo, Irina

Abstract

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective lon

Published
2023

30. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

Author

Fu, Cynthia H Y, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen R, Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N, Frokjaer, Vibe G, Ganz, Melanie, Garcia, Jose, Godlewska, Beata R, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C, Tosun, Duygu, Victor, Teresa A, Wei, Dongtao, Wise, Toby, Woodham, Rachel D, Zahn, Roland, Anderson, Ian M, Deakin, J F William, Dunlop, Boadie W, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H, Harmer, Catherine J, Kennedy, Sidney H, Knudsen, Gitte M, Mayberg, Helen S, Paulus, Martin P, Qiu, Jiang, Trivedi, Madhukar H, Whalley, Heather C, Yan, Chao-Gan, Young, Allan H, Davatzikos, Christos, Fu, Cynthia H Y, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen R, Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N, Frokjaer, Vibe G, Ganz, Melanie, Garcia, Jose, Godlewska, Beata R, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C, Tosun, Duygu, Victor, Teresa A, Wei, Dongtao, Wise, Toby, Woodham, Rachel D, Zahn, Roland, Anderson, Ian M, Deakin, J F William, Dunlop, Boadie W, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H, Harmer, Catherine J, Kennedy, Sidney H, Knudsen, Gitte M, Mayberg, Helen S, Paulus, Martin P, Qiu, Jiang, Trivedi, Madhukar H, Whalley, Heather C, Yan, Chao-Gan, Young, Allan H, and Davatzikos, Christos

Abstract

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on

Published
2023

31. General and specific patterns of cortical gene expression as substrates of complex cognitive functioning

Author

Joanna E. Moodie, Sarah E. Harris, Mat A. Harris, Colin R. Buchanan, Gail Davies, Adele Taylor, Paul Redmond, David Liewald, Maria del C Valdés Hernández, Susan Shenkin, Tom C. Russ, Susana Muñoz Maniega, Michelle Luciano, Janie Corley, Aleks Stolicyn, Xueyi Shen, Douglas Steele, Gordon Waiter, Anca Sandu-Giuraniuc, Mark E. Bastin, Joanna M. Wardlaw, Andrew McIntosh, Heather Whalley, Elliot M. Tucker-Drob, Ian J. Deary, and Simon R. Cox

Subjects
Article
Abstract

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, general rules may govern shared spatial fluctuations in expression across the genome. Such information would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. We find that the region-to-region variation in cortical expression profiles of 8235 genes covary across two major dimensions: cell-signalling/modification and transcription factors. These patterns are validated out-of-sample and across different data processing choices. Brain regions most strongly implicated in general cognitive ability (g; meta-analyticN= 40,929) have greater balance between downregulation and upregulation of both major components. We identify a further 34 genes as candidate substrates ofg. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

Published
2023

32. Classification accuracy of structural and functional connectomes across different depressive phenotypes

Author

Yeung, Hon Wah, primary, Stolicyn, Aleks, additional, Shen, Xueyi, additional, Adams, Mark J., additional, Romaniuk, Liana, additional, Thng, Gladi, additional, Buchanan, Colin R., additional, Tucker-Drob, Elliot M., additional, Bastin, Mark E., additional, McIntosh, Andrew M., additional, Cox, Simon R., additional, Smith, Keith M., additional, and Whalley, Heather C., additional

Published
2023
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33. Exploring psychiatric conditions through machine learning: a comprehensive analysis of large-scale data from the ECNP data network

Author

Khuntia, A., primary, Buciuman, M., additional, Fanning, J., additional, Herrera, A., additional, Wiegand, A., additional, Hahn, L., additional, From, T., additional, Goffi, F., additional, Kaufmann, T., additional, Laurikainen, L., additional, Maggioni, E., additional, Martinez, I., additional, Stolicyn, A., additional, Schwarz, E., additional, Squarcina, L., additional, Andreassen, O., additional, Bellani, M., additional, Brambilla, P., additional, Hietala, J., additional, Lawrie, S., additional, Soriano, S., additional, Whalley, H., additional, and Koutsouleris, N., additional

Published
2023
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34. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Antoniades, Mathilde, primary, Fu, Cynthia, additional, Erus, Guray, additional, Garcia, Jose, additional, Fan, Yong, additional, Arnone, Danilo, additional, Arnott, Stephen, additional, Chen, Taolin, additional, Choi, Ki Sueng, additional, Fatt, Cherise Chin, additional, Frey, Benicio, additional, Frokjaer, Vibe, additional, Ganz, Melanie, additional, Godlewska, Beata, additional, Hassel, Stefanie, additional, Ho, Keith, additional, McIntosh, Andrew, additional, Qin, Kun, additional, Rotzinger, Susan, additional, Sacchet, Matthew, additional, Savitz, Jonathan, additional, Shou, Haochang, additional, Singh, Ashish, additional, Stolicyn, Aleks, additional, Strigo, Irina, additional, Strother, Stephen, additional, Tosun, Duygu, additional, Victor, Teresa, additional, Wei, Dongtao, additional, Wise, Toby, additional, Zahn, Roland, additional, Anderson, Ian, additional, Deakin, J.F. William, additional, Dunlop, Boadie, additional, Elliott, Rebecca, additional, Gong, Qiyong, additional, Gotlib, Ian, additional, Harmer, Catherine, additional, Kennedy, Sidney, additional, Knudsen, Gitte, additional, Mayberg, Helen, additional, Paulus, Martin, additional, Qiu, Jiang, additional, Trivedi, Madhukar, additional, Whalley, Heather, additional, Yan, Chao-Gan, additional, Young, Allan, additional, and Davatzikos, Christos, additional

Published
2022
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36. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

Author

Cynthia H. Y. Fu, Guray Erus, Yong Fan, Mathilde Antoniades, Danilo Arnone, Stephen R. Arnott, Taolin Chen, Ki Sueng Choi, Cherise Chin Fatt, Benicio N. Frey, Vibe G. Frokjaer, Melanie Ganz, Jose Garcia, Beata R. Godlewska, Stefanie Hassel, Keith Ho, Andrew M. McIntosh, Kun Qin, Susan Rotzinger, Matthew D. Sacchet, Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina Strigo, Stephen C. Strother, Duygu Tosun, Teresa A. Victor, Dongtao Wei, Toby Wise, Rachel D. Woodham, Roland Zahn, Ian M. Anderson, J. F. William Deakin, Boadie W. Dunlop, Rebecca Elliott, Qiyong Gong, Ian H. Gotlib, Catherine J. Harmer, Sidney H. Kennedy, Gitte M. Knudsen, Helen S. Mayberg, Martin P. Paulus, Jiang Qiu, Madhukar H. Trivedi, Heather C. Whalley, Chao-Gan Yan, Allan H. Young, and Christos Davatzikos

Subjects
Depression, Predictors, Reproducibility of Results, Brain, Deep learning, Neuroimaging, Depressive Disorder, Major/diagnosis, Classification, Magnetic Resonance Imaging/methods, Psychiatry and Mental health, Artificial Intelligence, Harmonization, Humans, Prospective Studies, Biomarkers, MRI
Abstract

Background Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. Methods We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. Results We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. Conclusion We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Published
2023

38. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Mathilde Antoniades, Cynthia Fu, Guray Erus, Jose Garcia, Yong Fan, Danilo Arnone, Stephen Arnott, Taolin Chen, Ki Sueng Choi, Cherise Chin Fatt, Benicio Frey, Vibe Frokjaer, Melanie Ganz, Beata Godlewska, Stefanie Hassel, Keith Ho, Andrew McIntosh, Kun Qin, Susan Rotzinger, Matthew Sacchet, Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina Strigo, Stephen Strother, Duygu Tosun, Teresa Victor, Dongtao Wei, Toby Wise, Roland Zahn, Ian Anderson, J.F. William Deakin, Boadie Dunlop, Rebecca Elliott, Qiyong Gong, Ian Gotlib, Catherine Harmer, Sidney Kennedy, Gitte Knudsen, Helen Mayberg, Martin Paulus, Jiang Qiu, Madhukar Trivedi, Heather Whalley, Chao-Gan Yan, Allan Young, and Christos Davatzikos

Abstract

Importance: Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Identifying neuroimaging-based biomarkers might aid in defining the disease-related dimensions that characterize MDD and predict treatment response. Objective: To investigate the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressant or placebo. Design: Big data consortium (COORDINATE-MDD) sharing raw MRI data in first episode and recurrent MDD, deep clinical phenotyping, and state-of-the art machine learning analysis, involving harmonization of multi-center MRI data and the application of semi-supervised machine learning clustering, HYDRA, to regional brain volumes. Setting: International, multi-center, community-based MDD and healthy controls. Participants: International sample (N=1384), consisting of medication-free, first episode and recurrent MDD individuals (N=685) in a current depressive episode of moderate to severe intensity, that is not treatment resistant depression, and healthy controls (N=699). Prospective longitudinal treatment response data were available in a subset of MDD individuals (N=359 MDD). Treatments were SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Treatment duration was 6-8 weeks, and symptom severity was measured by clinician-rated scales. Main outcomes: First episode and recurrent MDD is optimally characterized by two neuroanatomical dimensions, which show distinct treatment effects to placebo and SSRI antidepressant medications. Results: Dimension 1 is characterized by preserved gray and white matter (N=290 MDD), whereas Dimension 2 is characterized by widespread subtle reductions in gray and white matter (N=395 MDD) relative to healthy controls. There are no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, but there is a significant dimension by treatment response interaction effect. Dimension 1 shows a significant decrease in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%), whereas Dimension 2 shows a comparable improvement to either SSRI (46.9%) or placebo (42.2%) (β=-18.3, 95% CI (-0.34 to -0.2), p=0.03). Conclusions and Relevance: Neuroimaging-based markers may aid in characterizing the MDD dimensions that predict treatment response. In an iterative process, we can characterize the disease-based dimensions that comprise MDD.

Published
2022

39. Brain charts for the human lifespan

Author

Armin Raznahan, Eric Courchesne, Andrea Parolin Jackowski, Kamen A. Tsvetanov, Cameron T. Ellis, R.C. Gur, Bin Bae J, Park Mtm, Pedro A. Valdes-Sosa, Simon N. Vandekar, Jacob W. Vogel, Juan Zhou, Machteld Marcelis, Kiho Im, Patricia Ellen Grant, Minhui Ouyang, Blesa Cabez M, Michael V. Lombardo, Sarah E. Morgan, James P. Boardman, Adamson C, Calhoun Vd, Delarue M, James H. Cole, Pichet Binette A, Roberto Toro, David H. Rowitch, Nynke A. Groenewold, Kevin M. Anderson, David T.W. Jones, Michael Schöll, Wang Ys, Aiden Corvin, R.E. Gur, Damien A. Fair, Gareth Ball, Herma Lina Schaare, Andrew Zalesky, Evdokia Anagnostou, Michael J. Meaney, Taki Y, Gareth J. Sullivan, Warrier, Petra E. Vértes, Chixiang Chen, Lisa T. Eyler, Wei Liao, Tomáš Paus, Jeremy A. Elman, Phillip McGuire, Hisham Ziauddeen, William S. Kremen, Etienne Vachon-Presseau, E.T. Bullmore, Christophe Tzourio, White, Hammill Cf, Mothersill D, Richard N. Henson, Jiang Qiu, Duncan E. Astle, Fabrice Crivello, Paul C. Fletcher, Chertavian C, Kim K, Jennifer Crosbie, Russell Schachar, Gabriel A. Devenyi, Manfred G. Kitzbichler, Tianye Jia, Trey Hedden, Sang Jae Lee, Ross D. Markello, Silke Kern, Ian M. Goodyer, Keith A. Johnson, Frauke Beyer, Bernard Mazoyer, A. Heinz, Sylvane Desrivières, Rosenberg, Gary Donohoe, Ong Mq, Alexander D. Edwards, Dan J. Stein, Nenad Medic, Zuo Xn, Travis T. Mallard, Peter Fonagy, Lindsay W. Victoria, Ingmar Skoog, Avram J. Holmes, Jason P. Lerch, Jed T. Elison, Jianfu Li, John H. Gilmore, Rosemary Holt, Caitlin K. Rollins, Carol E. Franz, Pedro Mario Pan, Saashi A Bedford, Yang N, Jonathan C Ipser, Richard A. I. Bethlehem, Tuulari Jj, Stolicyn A, Hua Huang, Bratislav Misic, Conor Liston, Ayub M, Lisa Ronan, Yeo Bt, Sophie Adler, Charles J. Lynch, Faith M. Gunning, Konrad Wagstyl, M. Mallar Chakravarty, John Suckling, Theodore D. Satterthwaite, Bharath Holla, Yap Seng Chong, Jinglei Lv, Jakob Seidlitz, Niall J Bourke, Xinlei Qian, Simon Baron-Cohen, Cynthia M. Ortinau, Deirel Paz Linares, Thyreau B, René S. Kahn, Aaron P. Schultz, Vanessa Cropley, Eric Westman, Mitchell Valdés-Sosa, Rik Ossenkoppele, André Zugman, Hasse Karlsson, Sylvia Villeneuve, Katja Heuer, Di Biase Ma, Margaret L. Westwater, Sofie L. Valk, David J. Sharp, Brigitte Landeau, Matthew Borzage, Kirsten A. Donald, Timothy Rittman, Richard Beare, Giovanni Abrahão Salum, Gunter Schumann, Ryuta Kawashima, Romero-Garcia R, John Blangero, Yun Hj, Russel T. Shinohara, Nicolas Crossley, Simon K. Warfield, Karen Pierce, George S. Alexopoulos, Katharine Dunlop, David C. Glahn, Francois Lalonde, Anqi Qiu, Lana Vasung, Gaël Chételat, Lídice Galán-García, Clifford R. Jack, Reisa A. Sperling, Anna Zettergren, Elizabeth Kelley, Arno Villringer, Andrea Mechelli, Benegal, Aaron Alexander-Bloch, Nicholas B. Turk-Browne, van Amelsvoort T, John D. Lewis, Heather C. Whalley, A. V. Witte, Zdenka Pausova, Joel T. Nigg, Heather J. Zar, Raymond J. Dolan, Christopher D. Smyser, Jay N. Giedd, Lena Palaniyappan, Ali Gholipour, Areces-Gonzalez A, Peter B. Jones, Jacqueline Hoare, Oskar Hansson, Linnea Karlsson, C Pantelis, Paly L, Bonnie Auyeung, Jorge Bosch-Bayard, Bethlehem, Richard [0000-0002-0714-0685], White, Simon [0000-0001-8642-7037], Astle, Duncan [0000-0002-7042-5392], Baron-Cohen, Simon [0000-0001-9217-2544], Henson, Rik [0000-0002-0712-2639], Jones, Peter [0000-0002-0387-880X], Kitzbichler, Manfred [0000-0002-4494-0753], Rittman, Timothy [0000-0003-1063-6937], Rowitch, David [0000-0002-0079-0060], Tsvetanov, Kamen A. [0000-0002-3178-6363], Westwater-Wozniak, Margaret [0000-0002-2918-0979], Ziauddeen, Hisham [0000-0003-4044-1719], Apollo - University of Cambridge Repository, British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, University of Cambridge [UK] (CAM), University of Pennsylvania, Yale University [New Haven], Institut des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Physiopathologie et imagerie des troubles neurologiques (PhIND), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Child and Adolescent Psychiatry Department [AP- HP Hôpital Robert Debré], AP-HP Hôpital universitaire Robert-Debré [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Département de Neuroscience - Department of Neuroscience, Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Neurology, Amsterdam Neuroscience - Neurodegeneration, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, The Mayo Clinic Study of Aging, NSPN, POND, The PREVENT-AD Research Group, VETSA, [Bethlehem, R. 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T.] Univ Cambridge, Dept Psychiat, Cambridge, England, [White, S. R.] Univ Cambridge, MRC Biostat Unit, Cambridge, England, [Vogel, J. W.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Satterthwaite, T. D.] Univ Penn, Lifespan Informat & Neuroimaging Ctr, Philadelphia, PA 19104 USA, [Anderson, K. M.] Yale Univ, Dept Psychol, New Haven, CT USA, [Ellis, C. T.] Yale Univ, Dept Psychol, New Haven, CT USA, [Turk-Browne, N. B.] Yale Univ, Dept Psychol, New Haven, CT USA, [Adamson, C.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Ball, G.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Beare, R.] Murdoch Childrens Res Inst, Dev Imaging, Melbourne, Vic, Australia, [Jackowski, A. 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E.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Henson, R. N.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Whalley, H. C.] Univ Cambridge, MRC Cognit & Brain Sci Unit, Cambridge, England, [Auyeung, B.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Pausova, Z.] Univ Edinburgh, Sch Philosophy Psychol & Language Sci, Dept Psychol, Edinburgh, Midlothian, Scotland, [Ayub, M.] Queens Univ, Dept Psychiat, Ctr Neurosci Studies, Kingston, ON, Canada, [Ayub, M.] UCL, Mental Hlth Neurosci Res Dept, Div Psychiat, London, England, [Bae, J.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Ball, G.] Univ Melbourne, Dept Paediat, Melbourne, Vic, Australia, [Baron-Cohen, S.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge Lifetime Asperger Syndrome Serv CLASS, Cambridge, England, [Benegal, V.] Natl Inst Mental Hlth & Neurosci NIMHANS, Ctr Addict Med, Bengaluru, India, [Beyer, F.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Villringer, A.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Witte, A. V.] Max Planck Inst Human Cognit & Brain Sci, Dept Neurol, Leipzig, Germany, [Blangero, J.] Univ Texas Rio Grande Valley, South Texas Diabet & Obes Inst, Dept Human Genet, Edinburg, TX USA, [Blesa Cabez, M.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Boardman, J. P.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Sullivan, G.] Univ Edinburgh, MRC Ctr Reprod Hlth, Edinburgh, Midlothian, Scotland, [Borzage, M.] Univ Southern Calif, Childrens Hosp Los Angeles, Keck Sch Med, Fetal & Neonatal Inst,Div Neonatol,Dept Pediat, Los Angeles, CA 90007 USA, [Bosch-Bayard, J. F.] Montreal Neurol Inst, Ludmer Ctr Neuroinformat & Mental Hlth, McGill Ctr Integrat Neurosci, Montreal, PQ, Canada, [Bosch-Bayard, J. F.] McGill Univ, Montreal, PQ, Canada, [Chakravarty, M. 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C.] Univ Cambridge, Dept Psychiat, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Wellcome Trust MRC Inst Metab Sci, Cambridge Biomed Campus, Cambridge, England, [Fletcher, P. C.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Jones, P. B.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Suckling, J.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Ziauddeen, H.] Cambridgeshire & Peterborough NHS Fdn Trust, Cambridge, England, [Fonagy, P.] UCL, Dept Clin Educ & Hlth Psychol, London, England, [Fonagy, P.] Anna Freud Natl Ctr Children & Families, London, England, [Galan-Garcia, L.] Cuban Ctr Neurosci, Havana, Cuba, [Valdes-Sosa, M. J.] Cuban Ctr Neurosci, Havana, Cuba, [Gholipour, A.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Warfield, S. K.] Boston Childrens Hosp, Computat Radiol Lab, Boston, MA USA, [Giedd, J.] Univ Calif San Diego, Dept Child & Adolescent Psychiat, San Diego, CA 92103 USA, [Giedd, J.] Univ Calif San Diego, Dept Psychiat, San Diego, CA 92103 USA, [Gilmore, J. H.] Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27515 USA, [Glahn, D. C.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Im, K.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Mathias, S. R.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Rodrigue, A.] Boston Childrens Hosp, Dept Psychiat, Boston, MA USA, [Glahn, D. C.] Harvard Med Sch, Boston, MA 02115 USA, [Im, K.] Harvard Med Sch, Boston, MA 02115 USA, [Johnson, K. A.] Harvard Med Sch, Boston, MA 02115 USA, [Mathias, S. R.] Harvard Med Sch, Boston, MA 02115 USA, [Rodrigue, A.] Harvard Med Sch, Boston, MA 02115 USA, [Schultz, A. P.] Harvard Med Sch, Boston, MA 02115 USA, [Sperling, R. A.] Harvard Med Sch, Boston, MA 02115 USA, [Grant, P. E.] Harvard Med Sch, Fetal Neonatal Neuroimaging & Dev Sci Ctr, Boston Childrens Hosp, Div Newborn Med & Neuroradiol, Boston, MA 02115 USA, [Groenewold, N. A.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Zar, H. J.] Univ Cape Town, SA MRC Unit Child & Adolescent Hlth, Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa, [Gunning, F. M.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Victoria, L. W.] Weill Cornell Med, Dept Psychiat, Weill Cornell Inst Geriatr Psychiat, New York, NY USA, [Hammill, C. F.] Mouse Imaging Ctr, Toronto, ON, Canada, [Hansson, O.] Lund Univ, Dept Clin Sci Malmo, Clin Memory Res Unit, Malmo, Sweden, [Hansson, O.] Skane Univ Hosp, Memory Clin, Malmo, Sweden, [Hedden, T.] Icahn Sch Med Mt Sinai, Dept Neurol, New York, NY 10029 USA, [Hedden, T.] Harvard Med Sch, Massachusetts Gen Hosp, Dept Radiol, Athinoula Martinos Ctr Biomed Imaging, Boston, MA 02115 USA, [Heinz, A.] Charite Univ Med Berlin, Charite Campus Mitte, Berlin, Germany, [Heinz, A.] Free Univ Berlin, Charite Campus Mitte, Berlin, Germany, [Heinz, A.] Humboldt Univ, Dept Psychiat & Psychotherapy, Charite Campus Mitte, Berlin, Germany, [Heuer, K.] Max Planck Inst Human Cognit & Brain Sci, Dept Neuropsychol, Leipzig, Germany, [Heuer, K.] Univ Paris, Paris, France, [Toro, R.] Univ Paris, Paris, France, [Hoare, J.] Univ Cape Town, Dept Psychiat, Cape Town, South Africa, [Holla, B.] NIMHANS, Dept Integrat Med, Bengaluru, India, [Holla, B.] NIMHANS, Dept Psychiat, Accelerator Program Discovery Brain Disorders Usi, Bengaluru, India, [Holmes, A. J.] Yale Univ, Dept Psychol, New Haven, CT USA, [Villeneuve, S.] Yale Univ, Dept Psychol, New Haven, CT USA, [Holmes, A. J.] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA, [Villeneuve, S.] Yale Univ, Dept Psychiat, New Haven, CT 06520 USA, [Huang, H.] Childrens Hosp Philadelphia, Radiol Res, Philadelphia, PA 19104 USA, [Ouyang, M.] Childrens Hosp Philadelphia, Radiol Res, Philadelphia, PA 19104 USA, [Huang, H.] Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA, [Ipser, J.] Univ Cape Town, Dept Psychiat & Mental Hlth, Clin Neurosci Inst, Cape Town, South Africa, [Jack, C. R., Jr.] Mayo Clin, Dept Radiol, Rochester, MN USA, [Jones, D. T.] Mayo Clin, Dept Radiol, Rochester, MN USA, Univ Fed Sao Paulo, Dept Psychiat, Sao Paulo, Brazil, [Jackowski, A. P.] Natl Inst Dev Psychiat, Beijing, Peoples R China, [Jia, T.] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China, [Jia, T.] Fudan Univ, Minist Educ, Key Lab Computat Neurosci & Brain Inspired Intell, Shanghai, Peoples R China, [Jia, T.] Kings Coll London, Inst Psychiat Psychol & Neurosci, Ctr Populat Neurosci & Precis Med PONS, SGDP Ctr, London, England, [Johnson, K. A.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Schultz, A. P.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Sperling, R. A.] Massachusetts Gen Hosp, Dept Neurol, Harvard Aging Brain Study, Boston, MA 02114 USA, [Johnson, K. A.] Brigham & Womens Hosp, Dept Neurol, Ctr Alzheimer Res & Treatment, 75 Francis St, Boston, MA 02115 USA, [Sperling, R. A.] Brigham & Womens Hosp, Dept Neurol, Ctr Alzheimer Res & Treatment, 75 Francis St, Boston, MA 02115 USA, [Johnson, K. A.] Massachusetts Gen Hosp, Dept Radiol, Boston, MA USA, [Jones, D. T.] Mayo Clin, Dept Neurol, Rochester, MN USA, [3R-BRAIN] Mayo Clin, Dept Neurol, Rochester, MN USA, [Kahn, R. S.] Icahn Sch Med Mt Sinai, Dept Psychiat, New York, NY 10029 USA, [Karlsson, H.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Karlsson, L.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Tuulari, J. J.] Univ Turku, Dept Psychiat, Dept Clin Med, Turku, Finland, [Karlsson, H.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Karlsson, L.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Tuulari, J. J.] Univ Turku, FinnBrain Birth Cohort Study, Turku Brain & Mind Ctr, Turku, Finland, [Karlsson, H.] Turku Univ Hosp, Turku, Finland, [Karlsson, L.] Turku Univ Hosp, Turku, Finland, [Tuulari, J. J.] Turku Univ Hosp, Turku, Finland, [Karlsson, H.] Turku Univ Hosp, Ctr Populat Hlth Res, Turku, Finland, [Karlsson, L.] Turku Univ Hosp, Ctr Populat Hlth Res, Turku, Finland, [Karlsson, H.] Univ Turku, Turku, Finland, [Karlsson, L.] Univ Turku, Turku, Finland, [Kawashima, R.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Taki, Y.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Thyreau, B.] Tohoku Univ, Inst Dev Aging & Canc, Aoba Ku, Sendai, Miyagi, Japan, [Kelley, E. A.] Queens Univ, Ctr Neurosci Studies, Dept Psychol, Kingston, ON, Canada, [Kelley, E. A.] Queens Univ, Ctr Neurosci Studies, Dept Psychiat, Kingston, ON, Canada, [Kern, S.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Skoog, I.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Zettergren, A.] Univ Gothenburg, Neuropsychiat Epidemiol Unit, Dept Psychiat & Neurochem,Sahlgrenska Acad, Ctr Ageing & Hlth AGECAP,Inst Neurosci & Physiol, Gothenburg, Sweden, [Kern, S.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Skoog, I.] Sahlgrens Univ Hosp, Psychiat Cognit & Old Age Psychiat Clin, Reg Vastra Gotaland, Gothenburg, Sweden, [Kim, K. W.] Seoul Natl Univ, Dept Brain & Cognit Sci, Coll Nat Sci, Seoul, South Korea, [Kim, K. W.] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, Seongnam, South Korea, [Kim, K. W.] Seoul Natl Univ, Dept Psychiat, Coll Med, Seoul, South Korea, [Kim, K. W.] SNU MRC, Inst Human Behav Med, Seoul, South Korea, [Lalonde, F.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Raznahan, A.] NIMH, Sect Dev Neurogenom, Human Genet Branch, Bethesda, MD 20892 USA, [Lee, S.] Seoul Natl Univ, Coll Nat Sci, Dept Brain & Cognit Sci, Seoul, South Korea, [Lerch, J.] Univ Toronto, Dept Med Biophys, Toronto, ON, Canada, [Lerch, J.] Univ Oxford, Nuffield Dept Clin Neurosci, FMRIB, Wellcome Ctr Integrat Neuroimaging, Oxford, England, [Lewis, J. D.] McGill Univ, Montreal Neurol Inst, Montreal, PQ, Canada, [Li, J.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Liao, W.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Valdes-Sosa, P. A.] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, Chengdu, Peoples R China, [Liston, C.] Weill Cornell Med, Dept Psychiat, New York, NY USA, [Liston, C.] Weill Cornell Med, Brain & Mind Res Inst, New York, NY USA, [Lombardo, M. V.] Ist Italiano Tecnol, Ctr Neurosci & Cognit Syst UniTn, Lab Autism & Neurodev Disorders, Rovereto, Italy, [Lv, J.] Univ Sydney, Sch Biomed Engn, Sydney, NSW, Australia, [Lv, J.] Univ Sydney, Brain & Mind Ctr, Sydney, NSW, Australia, [Mallard, T. T.] Univ Texas Austin, Dept Psychol, Austin, TX 78712 USA, [Marcelis, M.] Maastricht Univ, Sch Mental Hlth & Neurosci, Dept Psychiat & Neuropsychol, EURON,Med Ctr, Maastricht, Netherlands, [Marcelis, M.] Inst Mental Hlth Care Eindhoven GGzE, Eindhoven, Netherlands, [Markello, R. D.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Misic, B.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Vasung, L.] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ, Canada, [Mazoyer, B.] Douglas Mental Hlth Univ Inst, Ludmer Ctr Neuroinformat & Mental Hlth, Montreal, PQ, Canada, [Meaney, M. J.] Douglas Mental Hlth Univ Inst, Ludmer Ctr Neuroinformat & Mental Hlth, Montreal, PQ, Canada, [Meaney, M. J.] Singapore Inst Clin Sci, Singapore, Singapore, [Mechelli, A.] Bordeaux Univ Hosp, Bordeaux, France, [Morgan, S. E.] Univ Cambridge, Dept Comp Sci & Technol, Cambridge, England, [Morgan, S. E.] Alan Turing Inst, London, England, [Vertes, P. E.] Alan Turing Inst, London, England, [Mothersill, D.] Natl Coll Ireland, Sch Business, Dept Psychol, Dublin, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Sch Psychol, Galway, Ireland, [Mothersill, D.] Natl Univ Ireland Galway, Ctr Neuroimaging & Cognit Genom, Galway, Ireland, [Mothersill, D.] Trinity Coll Dublin, Dept Psychiat, Dublin, Ireland, [Nigg, J.] Oregon Hlth & Sci Univ, Dept Psychiat, Sch Med, Portland, OR 97201 USA, [Ong, M. Q. W.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Qian, X.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Ortinau, C.] Washington Univ, Dept Pediat, St Louis, MO 63130 USA, [Ossenkoppele, R.] Vrije Univ Amsterdam, Alzheimer Ctr Amsterdam, Amsterdam UMC, Dept Neurol,Amsterdam Neurosci, Amsterdam, Netherlands, [Ossenkoppele, R.] Lund Univ, Clin Memory Res Unit, Lund, Sweden, [Palaniyappan, L.] Univ Western Ontario, Robarts Res Inst, London, ON, Canada, [Palaniyappan, L.] Univ Western Ontario, Brain & Mind Inst, London, ON, Canada, [Pan, P. M.] Fed Univ Sao Poalo UNIFESP, Dept Psychiat, Sao Poalo, Brazil, [Pan, P. M.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Zugman, A.] Natl Inst Dev Psychiat Children & Adolescents INP, Sao Poalo, Brazil, [Pantelis, C.] Univ Melbourne, Dept Psychiat, Melbourne Neuropsychiat Ctr, Carlton, Vic, Australia, [Pantelis, C.] Melbourne Hlth, Carlton, Vic, Australia, [Pantelis, C.] Univ Melbourne, Melbourne Sch Engn, Parkville, Vic, Australia, [Pantelis, C.] Florey Inst Neurosci & Mental Hlth, Parkville, Vic, Australia, [Park, M. M.] Western Univ, Schulich Sch Med & Dent, Dept Psychiat, London, ON, Canada, [Rollins, C. K.] Univ Montreal, Dept Psychiat, Fac Med, Montreal, PQ, Canada, [Rollins, C. K.] Univ Montreal, CHU St Justine, Montreal, PQ, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychiat, Toronto, ON, Canada, [Romero-Garcia, R.] Univ Toronto, Dept Psychol, Toronto, ON, Canada, [Rosenberg, M. D.] Univ Toronto, Dept Physiol, Toronto, ON, Canada, [Rosenberg, M. D.] Univ Toronto, Dept Nutr Sci, Toronto, ON, Canada, [Paz-Linares, D.] Cuban Neurosci Ctr, Havana, Cuba, [Pichet Binette, A.] McGill Univ, Fac Med, Dept Psychiat, Montreal, PQ, Canada, [Villeneuve, S.] McGill Univ, Fac Med, Dept Psychiat, Montreal, PQ, Canada, [Pichet Binette, A.] Douglas Mental Hlth Univ Inst, Montreal, PQ, Canada, [Villeneuve, S.] Douglas Mental Hlth Univ Inst, Montreal, PQ, Canada, [Qiu, J.] Southwest Univ, Sch Psychol, Chongqing, Peoples R China, [Qiu, A.] Natl Univ Singapore, N1 Inst Hlth, Dept Biomed Engn, Singapore, Singapore, [Rittman, T.] Univ Cambridge, Dept Clin Neurosci, Cambridge, England, [Tsvetanov, K. A.] Univ Cambridge, Dept Clin Neurosci, Cambridge, England, [Rollins, C. K.] Harvard Med Sch, Dept Neurol, Boston, MA 02115 USA, [Rollins, C. K.] Boston Childrens Hosp, Dept Neurol, Boston, MA USA, [Romero-Garcia, R.] Univ Seville, Dpto Fisiol Med & Biofis, Inst Biomed Sevilla IBiS HUVR CSIC, Seville, Spain, [Rosenberg, M. D.] Univ Chicago, Dept Psychol, 5848 S Univ Ave, Chicago, IL 60637 USA, [Rosenberg, M. D.] Univ Chicago, Inst Neurosci, Chicago, IL USA, [Rowitch, D. H.] Univ Cambridge, Dept Paediat, Cambridge, England, [Rowitch, D. H.] Univ Cambridge, Wellcome MRC Cambridge Stem Cell Inst, Cambridge, England, [Salum, G. A.] Univ Fed Rio Grande Sul UFRGS, Hosp Clin Porto Alegre, Dept Psychiat, Porto Alegre, RS, Brazil, [Salum, G. A.] Natl Inst Dev Psychiat INPD, Sao Paulo, Brazil, [Schaare, H. L.] Max Planck Inst Human Cognit & Brain Sci, Otto Hahn Grp Cognit Neurogenet, Leipzig, Germany, [Schaare, H. L.] Res Ctr Juelich, Inst Neurosci & Med INM 7 Brain & Behav, Julich, Germany, [Schultz, A. P.] Massachusetts Gen Hosp, Dept Radiol, Athinoula Martinos Ctr Biomed Imaging, Charlestown, MA USA, [Schumann, G.] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Ctr Populat Neurosci & Stratified Med PONS, Shanghai, Peoples R China, [Schumann, G.] Charite Campus Mitte, Dept Psychiat & Psychotherapy, Charite Mental Hlth, PONS Ctr, Berlin, Germany, [Scholl, M.] Univ Gothenburg, Wallenberg Ctr Mol & Translat Med, Gothenburg, Sweden, [Scholl, M.] Univ Gothenburg, Dept Psychiat & Neurochem, Gothenburg, Sweden, [Scholl, M.] UCL, Queens Sq Inst Neurol, Dementia Res Ctr, London, England, [Sharp, D.] UK Dementia Res Inst, Care Res & Technol Ctr, London, England, [Shinohara, R. T.] Univ Penn, Perelman Sch Med, Dept Radiol, Ctr Biomed Image Comp & Analyt, Philadelphia, PA 19104 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Neurol, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Pediat, St Louis, MO 63110 USA, [Smyser, C. D.] Washington Univ, Sch Med, Dept Radiol, St Louis, MO 63110 USA, [Stein, D. J.] Univ Cape Town, Dept Psychiat, SA MRC Unit Risk & Resilience Mental Disorders, Cape Town, South Africa, [Stein, D. J.] Univ Cape Town, Neurosci Inst, Cape Town, South Africa, [Stolicyn, A.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Whalley, H. C.] Univ Edinburgh, Ctr Clin Brain Sci, Div Psychiat, Edinburgh, Midlothian, Scotland, [Toro, R.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Inst Pasteur, Dept Neurosci, Paris, France, [Traut, N.] Univ Paris 05, Ctr Res & Interdisciplinar CRI, Paris, France, [Tsvetanov, K. A.] Univ Cambridge, Dept Psychol, Cambridge, England, [Turk-Browne, N. B.] Yale Univ, Wu Tsai Inst, New Haven, CT USA, [Tuulari, J. J.] Univ Turku, Dept Clin Med, Turku, Finland, [Tuulari, J. J.] Univ Turku, Turku Coll Sci Med & Technol, Turku, Finland, [Tzourio, C.] Univ Bordeaux, Bordeaux Populat Hlth Res Ctr, CHU Bordeaux, U1219,INSERM, Bordeaux, France, [Vachon-Presseau, E.] McGill Univ, Fac Dent Med & Oral Hlth Sci, Montreal, PQ, Canada, [Valdes-Sosa, P. A.] McGill Univ, Alan Edwards Ctr Res Pain AECRP, Montreal, PQ, Canada, [Valk, S. L.] Forschungszentrum Julich, Inst Neurosci & Med 7, Julich, Germany, [Valk, S. L.] Max Planck Inst Human Cognit & Brain Sci, Leipzig, Germany, [van Amelsvoort, T.] Maastricht Univ, Dept Psychiat & Neurosychol, Maastricht, Netherlands, [Vandekar, S. N.] Vanderbilt Univ, Dept Biostat, 221 Kirkland Hall, Nashville, TN 37235 USA, [Villeneuve, S.] Vanderbilt Univ, Med Ctr, Dept Biostat, Nashville, TN USA, [Villringer, A.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Witte, A. V.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Zuo, X. N.] Univ Leipzig, Clin Cognit Neurol, Med Ctr, Leipzig, Germany, [Wang, Y. S.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Yeo, B.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China, [Wang, Y. S.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Yang, N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Zuo, X. N.] Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neuroscience Res Ctr, Beijing, Peoples R China, [Wang, Y. S.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Yang, N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Zuo, X. N.] Natl Basic Sci Data Ctr, Beijing, Peoples R China, [Wang, Y. S.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Yang, N.] Chinese Acad Sci, Res Ctr Lifespan Dev Brain & Mind, Inst Psychol, Beijing, Peoples R China, [Westman, E.] Karolinska Inst, Ctr Alzheimer Res, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden, [Witte, A. V.] Univ Leipzig, CRC 1052 Obes Mech, Fac Med, Leipzig, Germany, [Zhou, J. H.] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Sleep & Cognit, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat MR Res, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, N1 Inst Hlth, Singapore, Singapore, [Yeo, B.] Natl Univ Singapore, Inst Digital Med, Singapore, Singapore, [Yun, H.] Natl Univ Singapore, Integrat Sci & Engn Programme ISEP, Singapore, Singapore, [Zar, H. J.] Univ Melbourne, Dept Biomed Engn, Melbourne, Vic, Australia, [Zhou, J. H.] Natl Univ Singapore, Yong Loo Lin Sch Med, Ctr Translat Magnet Resonance Res, Singapore, Singapore, [Ziauddeen, H.] Univ Cambridge, Wellcome Trust MRC Inst Metab Sci, Cambridge, England, [Zugman, A.] NIMH, NIH, Bethesda, MD 20892 USA, [Zugman, A.] Escola Paulista Med, Dept Psychiat, Sao Paulo, Brazil, [Zuo, X. N.] Nanning Normal Univ, Sch Educ Sci, Key Lab Brain & Educ, Nanning, Peoples R China, British Academy Postdoctoral fellowship, NIMH, UKRI Medical Research Council, NIHR Cambridge Biomedical Research Centre, NIHR Senior Investigator award, MRC research infrastructure award, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Ontario Brain Institute

Subjects
631/378/2649, OpenPain Project, KNE96, Growth, Psychiatric-disorders, DISEASE, 3R-BRAIN, Brain charts, MRI Brain, OASIS-3, Disease, CCNP, 631/378/2571, UMN BCP, Multidisciplinary, medicine.diagnostic_test, PSYCHIATRIC-DISORDERS, article, Brain, Human brain, ASSOCIATION, Magnetic Resonance Imaging, Harvard Aging Brain Study, The Mayo Clinic Study of Aging, NSPN, medicine.anatomical_structure, GROWTH, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], ddc:500, BURDEN, WHITE-MATTER, FinnBrain, Harvard Aging Brain Study, Organization, Mri, MRI, medicine.medical_specialty, Concurrent validity, MODELS, Cam-CAN, Longevity, CALM Team, POND, Neuroimaging, Burden, ORGANIZATION, AIBL, The PREVENT-AD Research Group, VETSA, Cortical thickness, Association, Physical medicine and rehabilitation, FinnBrain, IMAGEN, KNE96, White-matter, medicine, Humans, ASRB, 631/378/1689, COBRE, business.industry, 631/378/2611, Brain morphometry, Neurosciences, Alzheimer’s Disease Repository Without Borders Investigators, Magnetic resonance imaging, Alzheimer’s Disease Neuroimaging Initiative, Anthropometry, Body Height, Brain growth, Birth, 59/57, Normative, IMAGEN, ENIGMA Developmental Brain Age working group, NSPN, business, CCNP, 3R-BRAIN, CORTICAL THICKNESS, Developing Human Connectome Project, ENIGMA Developmental Brain Age working group, The PREVENT-AD Research Group, VETSA, Bullmore, E.T
Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Published
2022

40. Epigenome-wide association study of global cortical volumes in generation Scotland: Scottish family health study

Author

Douglas Steele, Joanna M. Wardlaw, Miruna C. Barbu, Rosie M. Walker, Stewart W. Morris, Anca L. Sandu, Mark Adams, Christopher J. McNeil, Heather C. Whalley, Carmen Amador, Alison D. Murray, Claire Green, Harris Ma, Stolicyn Aleks, Kathryn L. Evans, Andrew M. McIntosh, David J. Porteous, Gordon D. Waiter, Archie Campbell, and Xueyi Shen

Subjects
Cancer Research, Population, Disease, Biology, Grey matter, Bioinformatics, Epigenesis, Genetic, Epigenome, 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, medicine, education, Molecular Biology, 030304 developmental biology, Genetic association, Family Health, 0303 health sciences, education.field_of_study, DNA methylation, cortical volumes, dNaM, DNA Methylation, epigenome-wide association study, Phenotype, medicine.anatomical_structure, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores.We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10-8, βrange = -1.46x10-6 to 9.59 × 10-7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level.These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.

Published
2021

41. Hair glucocorticoids are associated with childhood adversity, depressive symptoms and reduced global and lobar grey matter in Generation Scotland

Author

Gordon D. Waiter, Claire Green, Mathew A. Harris, J. Douglas Steele, Anca-Larisa Sandu, Joanna M. Wardlaw, David J. Porteous, Heather C. Whalley, Liana Romaniuk, Emma L. Hawkins, Rebecca M. Reynolds, Xueyi Shen, Miruna C. Barbu, Aleks Stolicyn, Jonathan R. Seckl, Andrew M. McIntosh, Stephen M. Lawrie, Jonathan Cavanagh, and Archie Campbell

Subjects
Hypothalamo-Hypophyseal System, Hydrocortisone, Physiology, Pituitary-Adrenal System, Context (language use), Neurosciences. Biological psychiatry. Neuropsychiatry, Grey matter, Article, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Neuroimaging, Adverse Childhood Experiences, medicine, Humans, Gray Matter, Glucocorticoids, Biological Psychiatry, Depression (differential diagnoses), Depressive Disorder, Major, business.industry, Depression, Middle Aged, medicine.disease, 030227 psychiatry, Psychiatry and Mental health, medicine.anatomical_structure, Etiology, Major depressive disorder, Cortisone, business, 030217 neurology & neurosurgery, medicine.drug, Neuroscience, RC321-571
Abstract

Hypothalamic–pituitary–adrenal (HPA) axis dysregulation has been commonly reported in major depressive disorder (MDD), but with considerable heterogeneity of results; potentially due to the predominant use of acute measures of an inherently variable/phasic system. Chronic longer-term measures of HPA-axis activity have yet to be systematically examined in MDD, particularly in relation to brain phenotypes, and in the context of early-life/contemporaneous stress. Here, we utilise a temporally stable measure of cumulative HPA-axis function (hair glucocorticoids) to investigate associations between cortisol, cortisone and total glucocorticoids with concurrent measures of (i) lifetime-MDD case/control status and current symptom severity, (ii) early/current-life stress and (iii) structural neuroimaging phenotypes, in N = 993 individuals from Generation Scotland (mean age = 59.1 yrs). Increased levels of hair cortisol were significantly associated with reduced global and lobar brain volumes with reductions in the frontal, temporal and cingulate regions (βrange = −0.057 to −0.104, all PFDR βrange = 0.071 to 0.115, all PFDR = β = 0.083, P = 0.017), and with reduced global and regional brain volumes (global: β = −0.059, P = 0.043; nucleus accumbens: β = −0.075, PFDR = 0.044). Associations with total glucocorticoids followed a similar pattern to the cortisol findings. In this large community-based sample, elevated glucocorticoids were significantly associated with MDD, with early, but not later-life stress, and with reduced global and regional brain phenotypes. These findings provide important foundations for future mechanistic studies to formally explore causal relationships between early adversity, chronic rather than acute measures of glucocorticoids, and neurobiological associations relevant to the aetiology of MDD.

Published
2021

42. Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability

Author

Gordon D. Waiter, Laura de Nooij, Simon R. Cox, Anca-Larisa Sandu, Mathew A. Harris, Andrew M. McIntosh, Alison D. Murray, Hon Wah Yeung, Archie Campbell, Colin R. Buchanan, J. Douglas Steele, Heather C. Whalley, Stephen M. Lawrie, David J. Porteous, Aleks Stolicyn, Liana Romaniuk, Xueyi Shen, Christopher J. McNeil, and Keith Smith

Subjects
Depressive Disorder, Major, General Neuroscience, Brain, Cognition, medicine.disease, Magnetic Resonance Imaging, Structural magnetic resonance imaging, Spectral clustering, Cohort, Covariate, medicine, Cluster Analysis, Humans, Major depressive disorder, Effects of sleep deprivation on cognitive performance, Cluster analysis, Psychology, Clinical psychology
Abstract

There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including Major Depressive Disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980), and UK Biobank (UKB, N = 8900); for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs, and graph partitioning with Markov Stability to determine optimal clustering of participants. Resultant clusters were i) checked whether they were replicated in an independent cohort, and ii) tested for associations with depression status, and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: pFDR = 0.2239 - 0.6585; UKB: pFDR = 0.2003 - 0.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets; for CSA, CV, subCV (GS subsample: d = 0.2529 - 0.3490, pFDR < 0.005; UKB: d = 0.0868 - 0.1070, pFDR < 0.005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.

Published
2021

43. Classification accuracy of structural and functional connectomes across different depressive phenotypes

Author

Hon Wah Yeung, Aleks Stolicyn, Xueyi Shen, Mark J. Adams, Liana Romaniuk, Gladi Thng, Colin R. Buchanan, Elliot M. Tucker-Drob, Mark E. Bastin, Andrew M. McIntosh, Simon R. Cox, Keith M. Smith, and Heather C. Whalley

Abstract

Phenotyping of major depressive disorder (MDD) in research can vary from study to study, which, together with heterogeneity of the disorder, may contribute to the inconsistent associations with various risk factors including neuroimaging features. These aspects also potentially underlie previous problems with machine learning methods using imaging data to inform predictive biomarkers. In this study we therefore aimed to examine the classification accuracy of structural and functional connectomes across different depressive phenotypes, including separating MDD subgroups into those with and without early childhood adversity (one of the largest risk factors for MDD associated with brain development). We applied logistic ridge regression to classify control and MDD participants defined according to six different MDD definitions in a large community-based sample (N= 14, 507). We used brain connectomic data based on six structural and two functional network weightings and conducted a comprehensive analysis to (i) explore how well different connectome modalities predict different MDD phenotypes commonly used in research, (ii) investigate whether stratification of MDD based on the presence or absence of early childhood adversity (measured with the childhood trauma questionnaire) can improve prediction accuracies, and (iii) identify important predictive features that are consistent across MDD phenotypes. We find that functional connectomes consistently outperform structural connectomes as features for MDD classification across phenotypes. Highest accuracy of 61.06% (chance level 50.0%) was achieved when predicting the Currently Depressed phenotype (i.e. the phenotype defined by the presence of more than five symptoms of depression in the past two weeks) with features based on partial correlation functional connectomes. Accuracy of classifying Currently Depressed participants with added CTQ threshold criterion rose to 65.74%. Application of the Jaccard index to assess predictive feature overlap indicated that there were neurobiological differences between MDD patients with and without childhood adversity. Further to that, analysis of predictive features for different MDD phenotypes with binomial tests revealed sensorimotor and visual functional subnetworks as consistently important for prediction. Our results provide the basis for future research, and indicate that differences in sensorimotor and visual subnetworks may serve as important biomarkers of MDD.

Published
2022

44. Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Author

Hon Wah Yeung, Aleks Stolicyn, Colin R. Buchanan, Elliot M. Tucker‐Drob, Mark E. Bastin, Saturnino Luz, Andrew M. McIntosh, Heather C. Whalley, Simon R. Cox, and Keith Smith

Subjects
cognition, structural connectomes, general psychopathology, Neurology, Radiological and Ultrasound Technology, deep learning, Radiology, Nuclear Medicine and imaging, Neurology (clinical), Anatomy, diffusion tensor imaging
Abstract

There is increasing expectation that advanced, computationally expensive machine learning techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different machine learning algorithms from deep learning model (BrainNetCNN) to classical machine learning methods. We modelledN= 8, 183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided highest prediction accuracies in all prediction tasks. Deep learning did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from deep learning and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models’ strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Published
2022

45. Classification accuracy of structural and functional connectomes across different depressive phenotypes

Author

Yeung, Hon Wah, primary, Stolicyn, Aleks, additional, Shen, Xueyi, additional, Adams, Mark J., additional, Romaniuk, Liana, additional, Thng, Gladi, additional, Buchanan, Colin R., additional, Tucker-Drob, Elliot M., additional, Bastin, Mark E., additional, McIntosh, Andrew M., additional, Cox, Simon R., additional, Smith, Keith M., additional, and Whalley, Heather C., additional

Published
2022
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46. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Yash Patel, Jean Shin, Christoph Abé, Ingrid Agartz, Clara Alloza, Dag Alnæs, Sonia Ambrogi, Linda A. Antonucci, Celso Arango, Volker Arolt, Guillaume Auzias, Rosa Ayesa-Arriola, Nerisa Banaj, Tobias Banaschewski, Cibele Bandeira, Zeynep Başgöze, Renata Basso Cupertino, Claiton H.D. Bau, Jochen Bauer, Sarah Baumeister, Fabio Bernardoni, Alessandro Bertolino, Caterina del Mar Bonnin, Daniel Brandeis, Silvia Brem, Jason Bruggemann, Robin Bülow, Juan R. Bustillo, Sara Calderoni, Rosa Calvo, Erick J. Canales-Rodríguez, Dara M. Cannon, Susanna Carmona, Vaughan J. Carr, Stanley V. Catts, Sneha Chenji, Qian Hui Chew, David Coghill, Colm G. Connolly, Annette Conzelmann, Alexander R. Craven, Benedicto Crespo-Facorro, Kathryn Cullen, Andreas Dahl, Udo Dannlowski, Christopher G. Davey, Christine Deruelle, Covadonga M. Díaz-Caneja, Katharina Dohm, Stefan Ehrlich, Jeffery Epstein, Tracy Erwin-Grabner, Lisa T. Eyler, Jennifer Fedor, Jacqueline Fitzgerald, William Foran, Judith M. Ford, Lydia Fortea, Paola Fuentes-Claramonte, Janice Fullerton, Lisa Furlong, Louise Gallagher, Bingchen Gao, Si Gao, Jose M. Goikolea, Ian Gotlib, Roberto Goya-Maldonado, Hans J. Grabe, Melissa Green, Eugenio H. Grevet, Nynke A. Groenewold, Dominik Grotegerd, Oliver Gruber, Jan Haavik, Tim Hahn, Ben J. Harrison, Walter Heindel, Frans Henskens, Dirk J. Heslenfeld, Eva Hilland, Pieter J. Hoekstra, Sarah Hohmann, Nathalie Holz, Fleur M. Howells, Jonathan C. Ipser, Neda Jahanshad, Babette Jakobi, Andreas Jansen, Joost Janssen, Rune Jonassen, Anna Kaiser, Vasiliy Kaleda, James Karantonis, Joseph A. King, Tilo Kircher, Peter Kochunov, Sheri-Michelle Koopowitz, Mikael Landén, Nils Inge Landrø, Stephen Lawrie, Irina Lebedeva, Beatriz Luna, Astri J. Lundervold, Frank P. MacMaster, Luigi A. Maglanoc, Daniel H. Mathalon, Colm McDonald, Andrew McIntosh, Susanne Meinert, Patricia T. Michie, Philip Mitchell, Ana Moreno-Alcázar, Bryan Mowry, Filippo Muratori, Leila Nabulsi, Igor Nenadić, Ruth O'Gorman Tuura, Jaap Oosterlaan, Bronwyn Overs, Christos Pantelis, Mara Parellada, Jose C. Pariente, Paul Pauli, Giulio Pergola, Francesco Maria Piarulli, Felipe Picon, Fabrizio Piras, Edith Pomarol-Clotet, Clara Pretus, Yann Quidé, Joaquim Radua, J. Antoni Ramos-Quiroga, Paul E. Rasser, Andreas Reif, Alessandra Retico, Gloria Roberts, Susan Rossell, Diego Luiz Rovaris, Katya Rubia, Matthew D. Sacchet, Josep Salavert, Raymond Salvador, Salvador Sarró, Akira Sawa, Ulrich Schall, Rodney Scott, Pierluigi Selvaggi, Tim Silk, Kang Sim, Antonin Skoch, Gianfranco Spalletta, Filip Spaniel, Dan J. Stein, Olaf Steinsträter, Aleks Stolicyn, Yoichiro Takayanagi, Leanne Tamm, Maria Tavares, Alexander Teumer, Katharina Thiel, Sophia I. Thomopoulos, David Tomecek, Alexander S. Tomyshev, Diana Tordesillas-Gutiérrez, Michela Tosetti, Anne Uhlmann, Tamsyn Van Rheenen, Javier Vazquez-Bourgón, Meike W. Vernooij, Eduard Vieta, Oscar Vilarroya, Cynthia Weickert, Thomas Weickert, Lars T. Westlye, Heather Whalley, David Willinger, Alexandra Winter, Katharina Wittfeld, Tony T. Yang, Yuliya Yoncheva, Jendé L. Zijlmans, Martine Hoogman, Barbara Franke, Daan van Rooij, Jan Buitelaar, Christopher R.K. Ching, Ole A. Andreassen, Elena Pozzi, Dick Veltman, Lianne Schmaal, Theo G.M. van Erp, Jessica Turner, F. Xavier Castellanos, Zdenka Pausova, Paul Thompson, Tomas Paus, Pediatric surgery, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, General Paediatrics, ARD - Amsterdam Reproduction and Development, Paediatrics, Radiology & Nuclear Medicine, Epidemiology, Clinical Cognitive Neuropsychiatry Research Program (CCNP), German Research Foundation, University of Münster, National Health and Medical Research Council (Australia), University of Cape Town, National Research Foundation (South Africa), Swinburne Universit, Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L. Cooper Foundation, Society of Mental Health Research, Swedish Research Council, Swedish Foundation for Strategic Research, Swedish Brain Foundation, Health Research Board (Ireland), Russian Foundation for Basic Research, University of Zurich, Pratt Foundation, Ramsay Health Care, Viertel Charitable Foundation, Schizophrenia Research Institute, European Commission, Australian Research Council, Instituto de Salud Carlos III, National Institute for Health and Care Research (US), National Institute for Health Research (UK), Ministry of Health of the Czech Republic, Bill & Melinda Gates Foundation, South African Medical Research Council, Carnegie Corporation of New York, Wellcome Trust, Medical Research Council (UK), Medical Research Scotland, Netherlands Organization for Scientific Research, Ambrogi, Sonia, Banaschewski, Tobias, Bandeira, Cibele Edom, Cupertino, Renata, Calderoni, Sara, Cannon, Dara, Carr, Vaughan, Chew, Qian Hui, Coghill, David, Cullen, Kathryn, Dahl, Andreas, Epstein, Jeffery, Foran, William, Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice M., Furlong, Lisa, Gallagher, Louise, Gao, Si, Gotlib, Ian, Haavik, Jan, Henskens, Frans, Hilland, Eva, Hoekstra, Pieter J, Howells, Fleur M, Ipser, Jonathan, Jørgensen, Jes Kristian, Karantonis, James A., Lawrie, Stephen, Research Institute of the Hospital for Sick Children and University of Toronto, University of Toronto, Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Canada, KG Jebsen Centre for Psychosis Research, University of Oslo (UiO)-Institute of Clinical Medicine-Oslo University Hospital [Oslo], Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Mental Health Sciences Unit, University College of London [London] (UCL), Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Clinical and Behavioral Neurology - Neuroscienze e riabilitazione, IRCCS Fondazione Santa Lucia [Roma], Heidelberg University, Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Universität Heidelberg [Heidelberg] = Heidelberg University, Clinical Neuropsychology, IBBA, APH - Mental Health, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Hospital Sant Joan de Déu [Barcelona], and Centro de Investigación Biomédica en Red Salud Mental [Madrid] (CIBER-SAM)

Subjects
Bipolar Disorder, Autism Spectrum Disorder, [SDV]Life Sciences [q-bio], Neurogenesis, pathology [Premature Birth], Neurodevelopment, Cortical surface area, pathology [Autism Spectrum Disorder], Cortical growth, methods [Magnetic Resonance Imaging], SDG 3 - Good Health and Well-being, Pregnancy, 130 000 Cognitive Neurology & Memory, Humans, ddc:610, Child, Biological Psychiatry, Cerebral Cortex, pathology [Depressive Disorder, Major], Depressive Disorder, Major, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Infant, Newborn, Magnetic Resonance Imaging, Mental illness, Attention Deficit Disorder with Hyperactivity, Premature Birth, genetics [Autism Spectrum Disorder], Female, Psychiatric disorders, 170 000 Motivational & Cognitive Control
Abstract

[Background]: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., [Methods]: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., [Results]: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., [Conclusions]: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., This work was supported by the German Research Foundation (DFG Grant Nos. HA7070/2-2, HA7070/3, HA7070/4 [to TH]) and IZKF of the medical faculty of Münster (Grants No. Dan3/012/17 [to UD] and MzH 3/020/20 [to TH]), and NHMRC projects (Grant No. 1064643 [to BJH] and 1024570 [to CGD]). The CIAM study was supported by the University of Cape Town Research Committee, South African National Research Foundation, and the South African Medical Research Council (principal investigator [PI], Fleur M. Howells) and Grant No. R01MH117601 (to NJ). This work was funded by the German Research Foundation (Grant Nos. FOR2107 JA 1890/7-1 and FOR2107 JA 1890/7-2 [to AJ]) and Swinburne University scholarship/Australian Postgraduate Award (to JK). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to JK). This work was funded by the German Research Foundation (Grant Nos. FOR2107 KI588/14-1 and FOR2107 KI588/14-2 [to TK]). The St. Göran study was supported by grants from the Swedish Research Council (Grant No. 2018-02653 [to ML]), the Swedish foundation for Strategic Research (Grant No. KF10-0039 [to ML]), the Swedish Brain foundation (Grant No. FO2020-0261 [to ML]), and the Swedish Government under the LUA/ALF agreement (Grant Nos. ALF 20170019 and ALFGBG-716801 [to ML]). This work was supported by RFBR (Grant No. 20-013-00748 [to IL, AST]) and funded by the Health Research Board (Grant No. HRA_POR/2011/100 [to CM]). The Australian Schizophrenia Research Bank (ASRB) was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This work was supported by Deutsche Forschungsgemeinschaft (Grant Nos. DFG NE 2254/2-1, NE 2254/3-1, NE2254/4-1 [to IN]), the University Research Priority Program “Integrative Human Physiology” at the University of Zurich (to ROT), an NHMRC Senior Principal Research Fellowship (Grant No. 1105825 [to CPa]), an NHMRC L3 Investigator Grant (Grant No. 1196508 [to CPa]), and NHMRC Program Grant (Grant No. 1150083 [to CPa]). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (Agreement No. 798181 [to GP]). ASRB was supported by the NHMRC (Enabling Grant, ID 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. The Imaging Genetics in Psychosis study was funded by Project Grants from the NHMRC (Grant Nos. APP630471 and APP1081603 [to YQ]) and the Macquarie University’s Australian Research Council (ARC) Centre of Excellence in Cognition and its Disorders (Grant No. CE110001021 [to YQ]). This work was supported by the Spanish Ministry of Science, Innovation and Universities/Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI11/01766 and CPII19/00009 [to JR]), co-financed by European Regional Development Fund funds from the European Commission (“A Way of Making Europe”). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. SR was supported by an NHMRC Senior Fellowship (Grant No. GNT1154651). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to SR). This work was supported by NIHR; MRC (to KR), NIH (Grant Nos. MH-094268, MH-105660, and MH-107730 [to ASS]). The Neuroimaging of the Children's Attention Project cohort was funded by NHMRC, Australia (Grant No. 1065895). Earlier funding for the cohort as also provided by NHMRC (Grant No. 1008522) and a grant from the Collier Foundation. The ACPU cohort was funded by NHMRC, Australia (Project Grant Nos. 384419 and 569533 [to TS]). This work was supported by research grants from the National Healthcare Group, Singapore (Grant Nos. SIG/05004, SIG/05028, and SIG /1103), and the Singapore Bioimaging Consortium (RP C009/2006 [to KS]), and the Ministry of Health, Czech Republic - conceptual development of research organization (“Institute for Clinical and Experimental Medicine - IKEM, IN 00023001” [to ASk]). This study was supported by the Italian Ministry of Health (Grant No. RC/17-18-19-20-21/A [to GS]) and Ministry of Health of the Czech Republic (Grant No. NU20-04-00393 [to FS]). The Drakenstein Child Health Study (DCHS) cohort is funded by the Bill and Melinda Gates Foundation (Grant No. OPP 1017641) and the South African Medical Research Council. This DCHS contribution was made possible in part by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author (DJS). STRADL study was supported and funded by the Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (Grant No. 104036/Z/14/Z), and the Medical Research Council Mental Health Pathfinder Award “Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders” (MRC, Grant No. MC_PC_17209). Scottish Bipolar Family Study (SBFS) was supported by National Health Service Research Scotland, through the Scottish Mental Health Research Network (www.smhrn.org.uk), who provided assistance with subject recruitment and assessments. SBFS was conducted at the Brain Research Imaging Centre (http://www.bric.ed.ac.uk), which is supported by SINAPSE (Scottish Imaging Network, a Platform for Scientific Excellence, http://www.sinapse.ac.uk). Processing of the datasets used the resources provided by the Edinburgh Compute and Data Facility (http://www.ecdf.ed.ac.uk/) (ASt). TVR was supported by an NHMRC Early Career Fellowship (Grant No. GNT1088785). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to TVR). EV was supported by the Spanish Ministry of Science and Innovation (PI18/00805) integrated into the Plan Nacional de I+D+I and co-financed by the ISCIII-Subdirección General de Evaluación and the FEDER; the Instituto de Salud Carlos III; the CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental); by the CERCA Programme/Generalitat de Catalunya and the Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement (Grant No. 2017SGR1355). This study was also supported by the Departament de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca i Innovació en Salut (PERIS) 2016-2020 (Grant No. SLT006/17/00345) and the European Union Horizon 2020 research and innovation program (EU.3.1.1. Understanding health, wellbeing and disease: Grant Nos. 754907 and EU.3.1.3 [to EB]; Treating and managing disease: Grant No. 945151 [to EV]). This study was supported by the National Center for Complementary and Integrative Health (Grant Nos. R21AT009173 and R61AT009864 [to TTY]); by the National Center for Advancing Translational Sciences (CTSI), National Institutes of Health, through UCSF-CTSI (Grant No. UL1TR001872 [to TTY]); by the American Foundation for Suicide Prevention (Grant No. SRG-1-141-18 [to TTY]); by UCSF Research Evaluation and Allocation Committee (REAC) and J. Jacobson Fund (to TTY); by the NIMH (Grant No. R01MH085734 [to TTY]); and by the Brain and Behavior Research Foundation (formerly NARSAD) (to TTY). This work was supported by a personal Veni grant to MH from the Netherlands Organization for Scientific Research (NWO, Grant No. 91619115 [to MH]) and European Community’s Horizon 2020 Programme (H2020/2014 – 2020) (Grant Agreements Nos. 667302 [CoCA], 728018 [Eat2beNICE], and 847879 [PRIME] [to BF]). JBu has been supported by the EU-AIMS (European Autism Interventions) and AIMS-2-TRIALS programmes, which receive support from Innovative Medicines Initiative Joint Undertaking Grant Nos. 115300 and 777394, the resources of which are composed of financial contributions from the European Union’s FP7 and Horizon 2020 Programmes, and from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions, and AUTISM SPEAKS, Autistica and SFARI; and by the Horizon 2020–supported programme CANDY (Grant No. 847818 [to JBu]). This work is supported by Grant No. NIA T32AG058507 and NIH Grant No. U54EB020403 from the Big Data to Knowledge (BD2K) Program (to CRKC). ENIGMA MDD work is supported by NIH (Grant Nos. U54 EB020403 [to PT], R01 MH116147 [to PT], and R01 MH117601 [to NJ and LS]). LS was supported by an NHMRC Career Development Fellowship (Grant No. 1140764). This work was supported by the National Center for Research Resources at the NIH (Grant Nos. NIH 1 U24 RR021992 [Function Biomedical Informatics Research Network], NIH 1 U24 RR025736-01 [Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org]). TGMvE is supported by ENIGMA’s NIH BD2K initiative (Grant No. U54 EB020403), ENIGMA Sex Differences (Grant No. R01MH116147), and ENIGMA-COINSTAC: Advanced Worldwide Transdiagnostic Analysis of Valence System Brain Circuits (Grant No. R01MH121246). This work was supported by the NIH (Grant No. R01MH121246 [to JT, Calhoun, and TGMvE]). This work was supported in part by NIH (Grant No. U54 EB020403 [to PT]).

Published
2022

47. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Patel, Yash, primary, Shin, Jean, additional, Abé, Christoph, additional, Agartz, Ingrid, additional, Alloza, Clara, additional, Alnæs, Dag, additional, Ambrogi, Sonia, additional, Antonucci, Linda A., additional, Arango, Celso, additional, Arolt, Volker, additional, Auzias, Guillaume, additional, Ayesa-Arriola, Rosa, additional, Banaj, Nerisa, additional, Banaschewski, Tobias, additional, Bandeira, Cibele, additional, Başgöze, Zeynep, additional, Cupertino, Renata Basso, additional, Bau, Claiton H.D., additional, Bauer, Jochen, additional, Baumeister, Sarah, additional, Bernardoni, Fabio, additional, Bertolino, Alessandro, additional, Bonnin, Caterina del Mar, additional, Brandeis, Daniel, additional, Brem, Silvia, additional, Bruggemann, Jason, additional, Bülow, Robin, additional, Bustillo, Juan R., additional, Calderoni, Sara, additional, Calvo, Rosa, additional, Canales-Rodríguez, Erick J., additional, Cannon, Dara M., additional, Carmona, Susanna, additional, Carr, Vaughan J., additional, Catts, Stanley V., additional, Chenji, Sneha, additional, Chew, Qian Hui, additional, Coghill, David, additional, Connolly, Colm G., additional, Conzelmann, Annette, additional, Craven, Alexander R., additional, Crespo-Facorro, Benedicto, additional, Cullen, Kathryn, additional, Dahl, Andreas, additional, Dannlowski, Udo, additional, Davey, Christopher G., additional, Deruelle, Christine, additional, Díaz-Caneja, Covadonga M., additional, Dohm, Katharina, additional, Ehrlich, Stefan, additional, Epstein, Jeffery, additional, Erwin-Grabner, Tracy, additional, Eyler, Lisa T., additional, Fedor, Jennifer, additional, Fitzgerald, Jacqueline, additional, Foran, William, additional, Ford, Judith M., additional, Fortea, Lydia, additional, Fuentes-Claramonte, Paola, additional, Fullerton, Janice, additional, Furlong, Lisa, additional, Gallagher, Louise, additional, Gao, Bingchen, additional, Gao, Si, additional, Goikolea, Jose M., additional, Gotlib, Ian, additional, Goya-Maldonado, Roberto, additional, Grabe, Hans J., additional, Green, Melissa, additional, Grevet, Eugenio H., additional, Groenewold, Nynke A., additional, Grotegerd, Dominik, additional, Gruber, Oliver, additional, Haavik, Jan, additional, Hahn, Tim, additional, Harrison, Ben J., additional, Heindel, Walter, additional, Henskens, Frans, additional, Heslenfeld, Dirk J., additional, Hilland, Eva, additional, Hoekstra, Pieter J., additional, Hohmann, Sarah, additional, Holz, Nathalie, additional, Howells, Fleur M., additional, Ipser, Jonathan C., additional, Jahanshad, Neda, additional, Jakobi, Babette, additional, Jansen, Andreas, additional, Janssen, Joost, additional, Jonassen, Rune, additional, Kaiser, Anna, additional, Kaleda, Vasiliy, additional, Karantonis, James, additional, King, Joseph A., additional, Kircher, Tilo, additional, Kochunov, Peter, additional, Koopowitz, Sheri-Michelle, additional, Landén, Mikael, additional, Landrø, Nils Inge, additional, Lawrie, Stephen, additional, Lebedeva, Irina, additional, Luna, Beatriz, additional, Lundervold, Astri J., additional, MacMaster, Frank P., additional, Maglanoc, Luigi A., additional, Mathalon, Daniel H., additional, McDonald, Colm, additional, McIntosh, Andrew, additional, Meinert, Susanne, additional, Michie, Patricia T., additional, Mitchell, Philip, additional, Moreno-Alcázar, Ana, additional, Mowry, Bryan, additional, Muratori, Filippo, additional, Nabulsi, Leila, additional, Nenadić, Igor, additional, O'Gorman Tuura, Ruth, additional, Oosterlaan, Jaap, additional, Overs, Bronwyn, additional, Pantelis, Christos, additional, Parellada, Mara, additional, Pariente, Jose C., additional, Pauli, Paul, additional, Pergola, Giulio, additional, Piarulli, Francesco Maria, additional, Picon, Felipe, additional, Piras, Fabrizio, additional, Pomarol-Clotet, Edith, additional, Pretus, Clara, additional, Quidé, Yann, additional, Radua, Joaquim, additional, Ramos-Quiroga, J. Antoni, additional, Rasser, Paul E., additional, Reif, Andreas, additional, Retico, Alessandra, additional, Roberts, Gloria, additional, Rossell, Susan, additional, Rovaris, Diego Luiz, additional, Rubia, Katya, additional, Sacchet, Matthew D., additional, Salavert, Josep, additional, Salvador, Raymond, additional, Sarró, Salvador, additional, Sawa, Akira, additional, Schall, Ulrich, additional, Scott, Rodney, additional, Selvaggi, Pierluigi, additional, Silk, Tim, additional, Sim, Kang, additional, Skoch, Antonin, additional, Spalletta, Gianfranco, additional, Spaniel, Filip, additional, Stein, Dan J., additional, Steinsträter, Olaf, additional, Stolicyn, Aleks, additional, Takayanagi, Yoichiro, additional, Tamm, Leanne, additional, Tavares, Maria, additional, Teumer, Alexander, additional, Thiel, Katharina, additional, Thomopoulos, Sophia I., additional, Tomecek, David, additional, Tomyshev, Alexander S., additional, Tordesillas-Gutiérrez, Diana, additional, Tosetti, Michela, additional, Uhlmann, Anne, additional, Van Rheenen, Tamsyn, additional, Vazquez-Bourgón, Javier, additional, Vernooij, Meike W., additional, Vieta, Eduard, additional, Vilarroya, Oscar, additional, Weickert, Cynthia, additional, Weickert, Thomas, additional, Westlye, Lars T., additional, Whalley, Heather, additional, Willinger, David, additional, Winter, Alexandra, additional, Wittfeld, Katharina, additional, Yang, Tony T., additional, Yoncheva, Yuliya, additional, Zijlmans, Jendé L., additional, Hoogman, Martine, additional, Franke, Barbara, additional, van Rooij, Daan, additional, Buitelaar, Jan, additional, Ching, Christopher R.K., additional, Andreassen, Ole A., additional, Pozzi, Elena, additional, Veltman, Dick, additional, Schmaal, Lianne, additional, van Erp, Theo G.M., additional, Turner, Jessica, additional, Castellanos, F. Xavier, additional, Pausova, Zdenka, additional, Thompson, Paul, additional, and Paus, Tomas, additional

Published
2022
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48. Brain charts for the human lifespan

Author

Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, Alexander-Bloch, AF, Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, and Alexander-Bloch, AF

Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Published
2022

49. Publisher Correction: Brain charts for the human lifespan.

Author

Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cábez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Schöll, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, É, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vértes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, Mayo Clinic Study of Aging, NSPN, POND, PREVENT-AD Research Group, VETSA, Bullmore, ET, Alexander-Bloch, AF, Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cábez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Schöll, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, É, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vértes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, Mayo Clinic Study of Aging, NSPN, POND, PREVENT-AD Research Group, VETSA, Bullmore, ET, and Alexander-Bloch, AF

Published
2022

50. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, Paus, T, Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, and Paus, T

Abstract

BACKGROUND: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life. METHODS: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed. RESULTS: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth. CONCLUSIONS: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from t

Published
2022

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